Computational Bioacoustics

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Ecology and Conservation".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 7310

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Guest Editor
Department of Computer Science, University of Milan, 20133 Milan, Italy
Interests: audio analyzing; AI; computer vision; robotics; deep learning
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Special Issue Information

Dear Colleagues,

Animals use acoustic vocalizations for reasons that are vital to their existence, such as mate attraction, territorial defense, and as an early warning to other members of the species regarding the presence of a dangerous predator. Animals produce a variety of sounds to communicate, ranging from short simple calls (also called pulses, syllables, or notes) to versatile long songs, which are composed of a complex hierarchy of syllables (very common in singing birds). The communication strategy and the diversity of the frequency structure of the sound pattern depends heavily on the environmental context and age of the living organism. Thus, solutions considering learning in non-stationary and evolving environments are the only possible option. In addition to vocalizations produced in the wild, understanding the mechanisms that regulate communication between animals and humans is particularly important for companion animals, such as dogs, cats, etc., who live in close contact with their human social partners and depend on them for health, care, and affection.

Despite the differences in sound generating apparatuses, animal sound patterns are usually handled in a common manner by means of signal processing and pattern recognition algorithms. This is because a sound source emits consistent acoustic patterns in a very distinctive and characteristic way to distribute its energy over time on its composing frequencies. The goal of this Special Issue is to thoroughly assess the relevance, usability, and performance of such AI-based solutions when applied to animal vocalizations. We are particularly interested in interpretable machine learning solutions from which the interested parties (e.g., biologists, ornithologists, psychologists, computer scientists, etc.) can gain clear insight regarding the problem at hand by analyzing the operation of the modeling approach.

We invite authors to submit original papers, communications, and review articles that focus on the latest advances covering the entire range of acoustic vocalizations, starting from modeling the animal sound production system to animal behavior understanding and acoustic biodiversity monitoring. Topics include, but are not limited to, the following:

  • computational auditory habitat analysis;
  • animal vocalization detection and recognition;
  • vocalizations of endangered species;
  • animal vocalization separation and localization;
  • sound production system modeling;
  • species identification;
  • acoustic farm monitoring;
  • natural soundscapes;
  • human–-animal communication;
  • tracking of similar breed animals and parturitions;
  • biodiversity and population monitoring;
  • identification of specific animal(s) for vaccination, medication, diseases, diet, etc. purposes;
  • audio-based animal health assessment;
  • interpretable machine- learning- based solutions;
  • intruder detection and identification;
  • methodologies, algorithms and techniques for learning in evolving auditory environments;
  • wireless acoustic sensor networks and related applications;
  • distributed audio signal processing and coding for segmentation, event detection and alerting.

Prof. Stavros Ntalampiras
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • computational auditory habitat analysis
  • animal vocalization detection and recognition
  • vocalizations of endangered species
  • animal vocalization separation and localization
  • sound production system modeling
  • species identification
  • acoustic farm monitoring
  • natural soundscapes
  • human–animal communication
  • tracking of similar breed animals and parturitions
  • biodiversity and population monitoring
  • identification of specific animal(s) for vaccination, medication, diseases, diet, etc. purposes
  • audio-based animal health assessment
  • interpretable machine-learning-based solutions
  • intruder detection and identification
  • methodologies, algorithms and techniques for learning in evolving auditory environments
  • wireless acoustic sensor networks and related applications
  • distributed audio signal processing and coding for segmentation, event detection and alerting

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Published Papers (2 papers)

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Research

12 pages, 7300 KiB  
Article
Great Himalayan Leaf-Nosed Bats Produce Different Territorial Calls to Respond to Sympatric Species and Non-Living Objects
by Hexuan Qin, Lei Feng, Xin Zhao, Congnan Sun, Jiang Feng and Tinglei Jiang
Animals 2020, 10(11), 2040; https://doi.org/10.3390/ani10112040 - 4 Nov 2020
Cited by 1 | Viewed by 2006
Abstract
Territorial signals are important for reducing the cost of territory defense. Normally, male animals will produce keep-out signals to repel intruders from entering their territory. However, there is currently no evidence that bats can adjust their territorial calls to respond differently to sympatric [...] Read more.
Territorial signals are important for reducing the cost of territory defense. Normally, male animals will produce keep-out signals to repel intruders from entering their territory. However, there is currently no evidence that bats can adjust their territorial calls to respond differently to sympatric species or non-living objects. In this study, we simulated the process of territory defense in male Great Himalayan leaf-nosed bats (Hipposideros armiger) toward two sympatric species (Hipposideros pratti and Rhinolophus sinicus) and four different non-living objects (a fur specimen of H. armiger, a bat model, a speaker, and a speaker with playback of H. armiger echolocation calls) to investigate their acoustic responses. There were significant differences in the territorial call complexity, syllable rate, and syllable ratio produced by H. armiger under the different experimental conditions. Our results confirmed that bats can adjust their territorial calls to respond to different sympatric species and non-living objects. The results will further our understanding of animal cognition and interactions among bat species from an acoustic perspective. Full article
(This article belongs to the Special Issue Computational Bioacoustics)
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13 pages, 3265 KiB  
Article
Uniform Manifold Approximation and Projection for Clustering Taxa through Vocalizations in a Neotropical Passerine (Rough-Legged Tyrannulet, Phyllomyias burmeisteri)
by Ronald M. Parra-Hernández, Jorge I. Posada-Quintero, Orlando Acevedo-Charry and Hugo F. Posada-Quintero
Animals 2020, 10(8), 1406; https://doi.org/10.3390/ani10081406 - 12 Aug 2020
Cited by 18 | Viewed by 4468
Abstract
Vocalizations from birds are a fruitful source of information for the classification of species. However, currently used analyses are ineffective to determine the taxonomic status of some groups. To provide a clearer grouping of taxa for such bird species from the analysis of [...] Read more.
Vocalizations from birds are a fruitful source of information for the classification of species. However, currently used analyses are ineffective to determine the taxonomic status of some groups. To provide a clearer grouping of taxa for such bird species from the analysis of vocalizations, more sensitive techniques are required. In this study, we have evaluated the sensitivity of the Uniform Manifold Approximation and Projection (UMAP) technique for grouping the vocalizations of individuals of the Rough-legged Tyrannulet Phyllomyias burmeisteri complex. Although the existence of two taxonomic groups has been suggested by some studies, the species has presented taxonomic difficulties in classification in previous studies. UMAP exhibited a clearer separation of groups than previously used dimensionality-reduction techniques (i.e., principal component analysis), as it was able to effectively identify the two taxa groups. The results achieved with UMAP in this study suggest that the technique can be useful in the analysis of species with complex in taxonomy through vocalizations data as a complementary tool including behavioral traits such as acoustic communication. Full article
(This article belongs to the Special Issue Computational Bioacoustics)
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